Pancreatic CancerAI-PANCNCT06638866
AI-powered Early Detection for Pancreatic Cancer Via Non-contrast CT in Opportunistic Screening Cohort
An observational study of Pancreatic Cancer and Pancreatic Ductal Adenocarcinoma, run by Changhai Hospital at 3 sites in China.
Trial summary
Pancreatic ductal adenocarcinoma (PDAC) remains a therapeutic challenge with 5-year survival rates of 13%, primarily attributable to advanced-stage diagnosis (AJCC Stage III/IV in >80% of cases). Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Pancreatic Cancer, Pancreatic Ductal Adenocarcinoma, Pancreatic Intraepithelial Neoplasias
- What is being tested?
- PDAC and Pancreatic precursor lesions
- How many people
- 5,000 participants
- Where
- 3 sitesChina
- Study dates
- 3 Aug 2024to 31 Dec 2030
- How long will I take part?
- 5 Years
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The study population included adults aged 18 years or older undergoing routine non-contrast chest and/or abdominal CT scans for non-pancreatic indications, while exclusion criteria comprised a history of pancreatic cancer, thoracic or abdominal surgery, acute pancreatitis within the past 6 months, or referral for evaluation of suspected or confirmed pancreatic cancer.
Who may be able to join
- 1. Individuals undergoing routine non-contrast chest and/or abdominal CT scans for non-pancreatic indications.
Who cannot join
- History of pancreatic cancer;
- History of thoracic or abdominal surgery;
- Acute pancreatitis within 6 months;
- Patients referred for evaluation of suspected or confirmed pancreatic cancer.
Trial locations and contacts
3 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China3 sites, 3 recruiting
- Recruiting
Second Affiliated Hospital of Jiaxing University
Jiaxing, Zhejiang, 314000
Shen Yi Jue, M.D., 13605835645, dr.syj@163.com
Shen Yi Jue, M.D.
- Recruiting
Yinzhou Hospital Affiliated to Medical School of Ningbo University
Ningbo, Zhejiang, 315100
Zhu Ke Lei, M.D., 13566636272, dr.zkl@163.com
Zhu Ke Lei, M.D.
- Recruiting
Shanghai Changhai Hospital
Shanghai, Shanghai Municipality, 200433
Wang Beilei, M.D., 86-13774238083, lilly_wang@126.com
Jin Gang, M.D.
About this study
Official title: Artificial Intelligence-based Health Information Management System and Key Technology Study of Early Screening and Hierarchical Diagnosis and Treatment of Pancreatic Cancer
Pancreatic ductal adenocarcinoma (PDAC) remains a therapeutic challenge with 5-year survival rates of 13%, primarily attributable to advanced-stage diagnosis (AJCC Stage III/IV in >80% of cases). This prospective, observational, multi-center study will evaluate the performance of an AI-powered opportunistic screening system utilizing non-contrast computed tomography (NCCT) acquired during routine clinical encounters or health check-ups. The proposed AI model will perform automated detection of pancreatic parenchymal abnormalities, including PDAC and precursor lesions (intraductal papillary mucinous neoplasms [IPMN], mucinous cystic neoplasms [MCN]). Algorithm-positive cases will be independently reviewed by two radiologists. Highly suspected individuals will undergo further diagnostic verification, including serological tests and multimodal imaging confirmation. Patients with confirmed positive diagnosis will receive multidisciplinary consultation and specialized treatment, whereas those with negative results will undergo at least one-year clinical follow-up. This study will quantitatively evaluate the AI system's performance, and aims to advance PDAC early detection, improve patient outcomes, and make it accessible in underserved populations.
Read the detailed description
PDAC is projected to become the second-leading cause of cancer mortality by 2030, with stage-specific survival disparities reaching 83.7% for stage IA versus 2.9% for stage IV disease. This dramatic survival gradient highlights the transformative potential of stage migration through early detection.
Screening-based early detection has demonstrated improved prognosis for PDAC patients; however, implementation faces dual challenges. he low incidence of PDAC renders population-wide screening cost-ineffective, while current screening methods are hampered by high false-positive rates and overdiagnosis risks. In this context, opportunistic screening has garnered attention for its unique implementation advantages. By leveraging existing imaging resources from routine clinical encounters or health check-ups, this approach obviates the need for additional screening infrastructure, potentially reducing healthcare resource consumption while effectively increasing screening coverage among high-risk populations.
Non-contrast computed tomography (NCCT), despite its widespread clinical application and operational convenience, is limited by suboptimal soft tissue resolution, resulting in insufficient sensitivity for early pancreatic lesions (≤2 cm), thus significantly constraining its utility in opportunistic screening. Recent advancements in AI technology have significantly impacted the field of medical image analysis. These techniques have enabled the automation of the detection of subtle pancreatic lesion features in large-scale imaging data, with the potential to enhance the accuracy and efficiency of early pancreatic cancer detection. In preliminary research, a deep learning-based model for pancreatic cancer detection was developed by our team. This model demonstrated the ability to accurately detect and classify pancreatic lesions on NCCT images, with excellent performance in multicenter validation studies. The model also exhibited strong generalizability when applied to chest CT scans. Therefore, AI-powered NCCT shows significant potential for application in hospital-based opportunistic screening programs and may become an effective tool for early pancreatic cancer detection. However, further research is required to fully explore and realize this potential.
This prospective, observational, multi-center study will evaluate the performance of an AI-powered opportunistic screening system utilizing NCCT acquired during routine clinical encounters or health check-ups. The deep learning-based detection system will perform automated identification of pancreatic lesions, including PDAC and precursor entities (intraductal papillary mucinous neoplasms [IPMN], mucinous cystic neoplasms [MCN]). Algorithm-positive cases will be independently reviewed by two radiologists. Individuals with high suspicion after radiologists review will undergo further validation via serological tests (e.g., CA19-9, CEA) and imaging studies (e.g., contrast-enhanced CT, contrast-enhanced MRI, EUS-FNA). Participants with a confirmed positive diagnosis will undergo multidisciplinary consultation and specialized treatment, while those with a negative diagnosis will be followed clinically for at least one year.
The AI system's performance will be evaluated through three primary metrics: (1) Detection rate of PDAC and high-risk precursor lesions, defined as the proportion of histologically confirmed PDAC and precursor lesions (IPMN/MCN) meeting Sendai criteria among all participants undergoing CT screening. (2) Recall rate, defined as the proportion of individuals recalled for confirmatory testing after AI-positive screening and radiologist review among all participants undergoing CT screening. (3) Positive predictive value (PPV) defined as the proportion of histologically confirmed PDAC and high-risk precursor lesions among all AI-positive screening cases.
Institutional Collaboration: Led by Shanghai Changhai Hospital (PI: Gang Jin, MD) with five regional centers (Yinzhou Hospital, Jiaxing University Hospital, Lishui Central Hospital, Jingning County Hospital) and Alibaba DAMO Academy (technical support).
- Conditions
- Pancreatic Cancer, Pancreatic Ductal Adenocarcinoma, Pancreatic Intraepithelial Neoplasias, Intraductal Papillary Mucinous Neoplasm, Mucinous Cystic Neoplasm
- Being tested
- PDAC, Pancreatic precursor lesions
- Keywords
- Early Screening, Early Diagnosis, Artificial Intelligence, Pancreatic Cancer, Pancreatic Lesion, PDAC, Non-contrast CT
- Medical subject headings
- Pancreatic Neoplasms, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
PDACDiagnostic test
Participants with algorithm-identified PDAC will be independently reviewed by two radiologists. Those highly suspected will be recalled for further diagnostic evaluation, including serological tests (e.g., CA19-9, CEA) and imaging (e.g., contrast-enhanced CT/MRI, EUS-FNA). Participants with a confirmed positive diagnosis will undergo multidisciplinary consultation and specialized treatment, while those with a negative diagnosis will be followed clinically for at least one year.
Pancreatic precursor lesionsDiagnostic test
Participants with algorithm-identified pancreatic precursor lesions will be independently reviewed by two radiologists. Those highly suspected will be recalled for further diagnostic evaluation, including serological tests (e.g., CA19-9, CEA) and imaging (e.g., contrast-enhanced CT/MRI, EUS-FNA). Participants with a confirmed positive diagnosis will undergo multidisciplinary consultation and specialized treatment, while those with a negative diagnosis will be followed clinically for at least one year.
| Study group | Role | What they receive |
|---|---|---|
| AIgorithm-classified PDAC Group | Participants who underwent non-contrast abdominal and/or chest CT scans and were preliminarily classified by the aIgorithm as PDAC. Receives: Diagnostic Test: PDAC | |
| AIgorithm-classified Pancreatic Precursor Lesions Group | Participants who underwent non-contrast abdominal and/or chest CT scans and were preliminarily classified by the aIgorithm as pancreatic precursor lesions. Receives: Diagnostic Test: Pancreatic precursor lesions |
What is being measured
The primary outcome is the main question the study is designed to answer.
Detection rate of PDAC
Defined as the proportion of histologically confirmed PDAC among all participants undergoing CT screening.
Measured: 3 years
Detection rate of high-risk precursor lesions
Defined as the proportion of histologically confirmed precursor lesions (IPMN/MCN) meeting Sendai criteria among all participants undergoing CT screening.
Measured: 3 years
PPV
Defined as the proportion of histologically confirmed PDAC and high-risk precursor lesions among all AI-positive screening cases.
Measured: 3 years
Recall rate
Defined as the proportion of individuals recalled for further validation via serological and imaging tests after AI-positive screening and radiologist review among all participants undergoing CT screening.
Measured: 3 years
Secondary outcomes (2)
Early-stage PDAC Proportion
Defined as the proportion of histologically confirmed early-stage PDAC among all PDAC cases detected through CT screening.
Measured: 3 years
Survival time
Defined as the survival time of patients with PDAC or precursor lesions detected through screening.
Measured: 5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Oct 2024
- Study start3 Aug 2024
- Primary completion31 Dec 2029 (estimated)
- Study completion31 Dec 2030 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (10)Papers the sponsor has linked to this study.
- Other trials for Pancreatic CancerStudies of the same condition, open and under way.
- More trials run by Changhai HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 5,000 participants (estimated)
- Target duration
- 5 Years
Sponsor and investigators
- Lead sponsor
- Changhai Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Yinzhou Hospital Affiliated to Medical School of Ningbo University, The Second Affiliated Hospital of Jiaxing University, Central Hospital of Lishui City, People's Hospital of Jingning She Autonomous County, Alibaba DAMO Academy
- Responsible party
- Sponsor
- Investigators
- Jin Gang, M.D., Study chair, Changhai HospitalWang Bei Lei, M.D., Study director, Changhai Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06638866
- Sponsor's protocol ID
- AI-PANC-1
- Other IDs
- 202401063, 202440208, 20511101200
- Acronym
- AI-PANC
- Status last verified
- Mar 2025
- First submitted
- 9 Oct 2024
- Last updated
- 19 Mar 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Chu LC, Park S, Kawamoto S, Wang Y, Zhou Y, Shen W, Zhu Z, Xia Y, Xie L, Liu F, Yu Q, Fouladi DF, Shayesteh S, Zinreich E, Graves JS, Horton KM, Yuille AL, Hruban RH, Kinzler KW, Vogelstein B, Fishman EK. Application of Deep Learning to Pancreatic Cancer Detection: Lessons Learned From Our Initial Experience. J Am Coll Radiol. 2019 Sep;16(9 Pt B):1338-1342. doi: 10.1016/j.jacr.2019.05.034. No abstract available.
PubMed 31492412Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, Tse D, Etemadi M, Ye W, Corrado G, Naidich DP, Shetty S. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019 Jun;25(6):954-961. doi: 10.1038/s41591-019-0447-x. Epub 2019 May 20.
PubMed 31110349Mizrahi JD, Surana R, Valle JW, Shroff RT. Pancreatic cancer. Lancet. 2020 Jun 27;395(10242):2008-2020. doi: 10.1016/S0140-6736(20)30974-0.
PubMed 32593337Pereira SP, Oldfield L, Ney A, Hart PA, Keane MG, Pandol SJ, Li D, Greenhalf W, Jeon CY, Koay EJ, Almario CV, Halloran C, Lennon AM, Costello E. Early detection of pancreatic cancer. Lancet Gastroenterol Hepatol. 2020 Jul;5(7):698-710. doi: 10.1016/S2468-1253(19)30416-9. Epub 2020 Mar 2.
PubMed 32135127Young MR, Abrams N, Ghosh S, Rinaudo JAS, Marquez G, Srivastava S. Prediagnostic Image Data, Artificial Intelligence, and Pancreatic Cancer: A Tell-Tale Sign to Early Detection. Pancreas. 2020 Aug;49(7):882-886. doi: 10.1097/MPA.0000000000001603.
PubMed 32675784Stoffel EM, Brand RE, Goggins M. Pancreatic Cancer: Changing Epidemiology and New Approaches to Risk Assessment, Early Detection, and Prevention. Gastroenterology. 2023 Apr;164(5):752-765. doi: 10.1053/j.gastro.2023.02.012. Epub 2023 Feb 18.
PubMed 36804602Kenner B, Chari ST, Kelsen D, Klimstra DS, Pandol SJ, Rosenthal M, Rustgi AK, Taylor JA, Yala A, Abul-Husn N, Andersen DK, Bernstein D, Brunak S, Canto MI, Eldar YC, Fishman EK, Fleshman J, Go VLW, Holt JM, Field B, Goldberg A, Hoos W, Iacobuzio-Donahue C, Li D, Lidgard G, Maitra A, Matrisian LM, Poblete S, Rothschild L, Sander C, Schwartz LH, Shalit U, Srivastava S, Wolpin B. Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review. Pancreas. 2021 Mar 1;50(3):251-279. doi: 10.1097/MPA.0000000000001762.
PubMed 33835956Klein AP. Pancreatic cancer epidemiology: understanding the role of lifestyle and inherited risk factors. Nat Rev Gastroenterol Hepatol. 2021 Jul;18(7):493-502. doi: 10.1038/s41575-021-00457-x. Epub 2021 May 17.
PubMed 34002083US Preventive Services Task Force; Owens DK, Davidson KW, Krist AH, Barry MJ, Cabana M, Caughey AB, Curry SJ, Doubeni CA, Epling JW Jr, Kubik M, Landefeld CS, Mangione CM, Pbert L, Silverstein M, Simon MA, Tseng CW, Wong JB. Screening for Pancreatic Cancer: US Preventive Services Task Force Reaffirmation Recommendation Statement. JAMA. 2019 Aug 6;322(5):438-444. doi: 10.1001/jama.2019.10232.
PubMed 31386141Cao K, Xia Y, Yao J, Han X, Lambert L, Zhang T, Tang W, Jin G, Jiang H, Fang X, Nogues I, Li X, Guo W, Wang Y, Fang W, Qiu M, Hou Y, Kovarnik T, Vocka M, Lu Y, Chen Y, Chen X, Liu Z, Zhou J, Xie C, Zhang R, Lu H, Hager GD, Yuille AL, Lu L, Shao C, Shi Y, Zhang Q, Liang T, Zhang L, Lu J. Large-scale pancreatic cancer detection via non-contrast CT and deep learning. Nat Med. 2023 Dec;29(12):3033-3043. doi: 10.1038/s41591-023-02640-w. Epub 2023 Nov 20.
PubMed 37985692
Study at a glance
From the registry record, last updated by the sponsor on 19 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-based Health Information Management System and Key Technology Study of Early Screening and Hierarchical Diagnosis and Treatment of Pancreatic Cancer
- ClinicalTrials.gov ID
- NCT06638866
- Acronym / sponsor ID
- AI-PANC / AI-PANC-1
- Conditions
- Pancreatic Cancer, Pancreatic Ductal Adenocarcinoma, Pancreatic Intraepithelial Neoplasias, Intraductal Papillary Mucinous Neoplasm, Mucinous Cystic Neoplasm
- Keywords
- Early Screening, Early Diagnosis, Artificial Intelligence, Pancreatic Cancer, Pancreatic Lesion, PDAC, Non-contrast CT
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 5,000 participants (estimated)
- Lead sponsor
- Changhai HospitalUniversity, hospital or other
- Collaborators
- Yinzhou Hospital Affiliated to Medical School of Ningbo University, The Second Affiliated Hospital of Jiaxing University, Central Hospital of Lishui City, People's Hospital of Jingning She Autonomous County, Alibaba DAMO Academy
- Study start
- 3 Aug 2024 (actual)
- Primary completion
- 31 Dec 2029 (estimated)
- Study completion
- 31 Dec 2030 (estimated)
- First posted
- 15 Oct 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Pancreatic ductal adenocarcinoma (PDAC) remains a therapeutic challenge with 5-year survival rates of 13%, primarily attributable to advanced-stage diagnosis (AJCC Stage III/IV in >80% of cases). This prospective, observational, multi-center study will evaluate the performance of an AI-powered opportunistic screening system utilizing non-contrast computed tomography (NCCT) acquired during routine clinical encounters or health check-ups. The proposed AI model will perform automated detection of pancreatic parenchymal abnormalities, including PDAC and precursor lesions (intraductal papillary mucinous neoplasms [IPMN], mucinous cystic neoplasms [MCN]). Algorithm-positive cases will be independently reviewed by two radiologists. Highly suspected individuals will undergo further diagnostic verification, including serological tests and multimodal imaging confirmation. Patients with confirmed positive diagnosis will receive multidisciplinary consultation and specialized treatment, whereas those with negative results will undergo at least one-year clinical follow-up. This study will quantitatively evaluate the AI system's performance, and aims to advance PDAC early detection, improve patient outcomes, and make it accessible in underserved populations.
Detailed description
PDAC is projected to become the second-leading cause of cancer mortality by 2030, with stage-specific survival disparities reaching 83.7% for stage IA versus 2.9% for stage IV disease. This dramatic survival gradient highlights the transformative potential of stage migration through early detection.
Screening-based early detection has demonstrated improved prognosis for PDAC patients; however, implementation faces dual challenges. he low incidence of PDAC renders population-wide screening cost-ineffective, while current screening methods are hampered by high false-positive rates and overdiagnosis risks. In this context, opportunistic screening has garnered attention for its unique implementation advantages. By leveraging existing imaging resources from routine clinical encounters or health check-ups, this approach obviates the need for additional screening infrastructure, potentially reducing healthcare resource consumption while effectively increasing screening coverage among high-risk populations.
Non-contrast computed tomography (NCCT), despite its widespread clinical application and operational convenience, is limited by suboptimal soft tissue resolution, resulting in insufficient sensitivity for early pancreatic lesions (≤2 cm), thus significantly constraining its utility in opportunistic screening. Recent advancements in AI technology have significantly impacted the field of medical image analysis. These techniques have enabled the automation of the detection of subtle pancreatic lesion features in large-scale imaging data, with the potential to enhance the accuracy and efficiency of early pancreatic cancer detection. In preliminary research, a deep learning-based model for pancreatic cancer detection was developed by our team. This model demonstrated the ability to accurately detect and classify pancreatic lesions on NCCT images, with excellent performance in multicenter validation studies. The model also exhibited strong generalizability when applied to chest CT scans. Therefore, AI-powered NCCT shows significant potential for application in hospital-based opportunistic screening programs and may become an effective tool for early pancreatic cancer detection. However, further research is required to fully explore and realize this potential.
This prospective, observational, multi-center study will evaluate the performance of an AI-powered opportunistic screening system utilizing NCCT acquired during routine clinical encounters or health check-ups. The deep learning-based detection system will perform automated identification of pancreatic lesions, including PDAC and precursor entities (intraductal papillary mucinous neoplasms [IPMN], mucinous cystic neoplasms [MCN]). Algorithm-positive cases will be independently reviewed by two radiologists. Individuals with high suspicion after radiologists review will undergo further validation via serological tests (e.g., CA19-9, CEA) and imaging studies (e.g., contrast-enhanced CT, contrast-enhanced MRI, EUS-FNA). Participants with a confirmed positive diagnosis will undergo multidisciplinary consultation and specialized treatment, while those with a negative diagnosis will be followed clinically for at least one year.
The AI system's performance will be evaluated through three primary metrics: (1) Detection rate of PDAC and high-risk precursor lesions, defined as the proportion of histologically confirmed PDAC and precursor lesions (IPMN/MCN) meeting Sendai criteria among all participants undergoing CT screening. (2) Recall rate, defined as the proportion of individuals recalled for confirmatory testing after AI-positive screening and radiologist review among all participants undergoing CT screening. (3) Positive predictive value (PPV) defined as the proportion of histologically confirmed PDAC and high-risk precursor lesions among all AI-positive screening cases.
Institutional Collaboration: Led by Shanghai Changhai Hospital (PI: Gang Jin, MD) with five regional centers (Yinzhou Hospital, Jiaxing University Hospital, Lishui Central Hospital, Jingning County Hospital) and Alibaba DAMO Academy (technical support).
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 5 Years
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AIgorithm-classified PDAC Group | – | Diagnostic Test: PDACParticipants who underwent non-contrast abdominal and/or chest CT scans and were preliminarily classified by the aIgorithm as PDAC. |
| AIgorithm-classified Pancreatic Precursor Lesions Group | – | Diagnostic Test: Pancreatic precursor lesionsParticipants who underwent non-contrast abdominal and/or chest CT scans and were preliminarily classified by the aIgorithm as pancreatic precursor lesions. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The study population included adults aged 18 years or older undergoing routine non-contrast chest and/or abdominal CT scans for non-pancreatic indications, while exclusion criteria comprised a history of pancreatic cancer, thoracic or abdominal surgery, acute pancreatitis within the past 6 months, or referral for evaluation of suspected or confirmed pancreatic cancer.
Key inclusion criteria (1 listed)
- 1. Individuals undergoing routine non-contrast chest and/or abdominal CT scans for non-pancreatic indications.
Key exclusion criteria (4 listed)
- History of pancreatic cancer;
- History of thoracic or abdominal surgery;
- Acute pancreatitis within 6 months;
- Patients referred for evaluation of suspected or confirmed pancreatic cancer.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| PDACParticipants with algorithm-identified PDAC will be independently reviewed by two radiologists. Those highly suspected will be recalled for further diagnostic evaluation, including serological tests (e.g., CA19-9, CEA) and imaging (e.g., contrast-enhanced CT/MRI, EUS-FNA). Participants with a confirmed positive diagnosis will undergo multidisciplinary consultation and specialized treatment, while those with a negative diagnosis will be followed clinically for at least one year. | Diagnostic test | AIgorithm-classified PDAC Group |
| Pancreatic precursor lesionsParticipants with algorithm-identified pancreatic precursor lesions will be independently reviewed by two radiologists. Those highly suspected will be recalled for further diagnostic evaluation, including serological tests (e.g., CA19-9, CEA) and imaging (e.g., contrast-enhanced CT/MRI, EUS-FNA). Participants with a confirmed positive diagnosis will undergo multidisciplinary consultation and specialized treatment, while those with a negative diagnosis will be followed clinically for at least one year. | Diagnostic test | AIgorithm-classified Pancreatic Precursor Lesions Group |
Primary outcome
Detection rate of PDAC
Time frame: 3 years
Defined as the proportion of histologically confirmed PDAC among all participants undergoing CT screening.
Detection rate of high-risk precursor lesions
Time frame: 3 years
Defined as the proportion of histologically confirmed precursor lesions (IPMN/MCN) meeting Sendai criteria among all participants undergoing CT screening.
PPV
Time frame: 3 years
Defined as the proportion of histologically confirmed PDAC and high-risk precursor lesions among all AI-positive screening cases.
Recall rate
Time frame: 3 years
Defined as the proportion of individuals recalled for further validation via serological and imaging tests after AI-positive screening and radiologist review among all participants undergoing CT screening.
2 secondary outcomes
Early-stage PDAC Proportion
Time frame: 3 years
Survival time
Time frame: 5 years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Mar 2025.
- Background: PubMed 31492412Chu LC, Park S, Kawamoto S, Wang Y, Zhou Y, Shen W, Zhu Z, Xia Y, Xie L, Liu F, Yu Q, Fouladi DF, Shayesteh S, Zinreich E, Graves JS, Horton KM, Yuille AL, Hruban RH, Kinzler KW, Vogelstein B, Fishman EK. Application of Deep Learning to Pancreatic Cancer Detection: Lessons Learned From Our Initial Experience. J Am Coll Radiol. 2019 Sep;16(9 Pt B):1338-1342. doi: 10.1016/j.jacr.2019.05.034. No abstract available.
- Background: PubMed 31110349Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, Tse D, Etemadi M, Ye W, Corrado G, Naidich DP, Shetty S. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019 Jun;25(6):954-961. doi: 10.1038/s41591-019-0447-x. Epub 2019 May 20.
- Background: PubMed 32593337Mizrahi JD, Surana R, Valle JW, Shroff RT. Pancreatic cancer. Lancet. 2020 Jun 27;395(10242):2008-2020. doi: 10.1016/S0140-6736(20)30974-0.
- Background: PubMed 32135127Pereira SP, Oldfield L, Ney A, Hart PA, Keane MG, Pandol SJ, Li D, Greenhalf W, Jeon CY, Koay EJ, Almario CV, Halloran C, Lennon AM, Costello E. Early detection of pancreatic cancer. Lancet Gastroenterol Hepatol. 2020 Jul;5(7):698-710. doi: 10.1016/S2468-1253(19)30416-9. Epub 2020 Mar 2.
- Background: PubMed 32675784Young MR, Abrams N, Ghosh S, Rinaudo JAS, Marquez G, Srivastava S. Prediagnostic Image Data, Artificial Intelligence, and Pancreatic Cancer: A Tell-Tale Sign to Early Detection. Pancreas. 2020 Aug;49(7):882-886. doi: 10.1097/MPA.0000000000001603.
- Background: PubMed 36804602Stoffel EM, Brand RE, Goggins M. Pancreatic Cancer: Changing Epidemiology and New Approaches to Risk Assessment, Early Detection, and Prevention. Gastroenterology. 2023 Apr;164(5):752-765. doi: 10.1053/j.gastro.2023.02.012. Epub 2023 Feb 18.
- Background: PubMed 33835956Kenner B, Chari ST, Kelsen D, Klimstra DS, Pandol SJ, Rosenthal M, Rustgi AK, Taylor JA, Yala A, Abul-Husn N, Andersen DK, Bernstein D, Brunak S, Canto MI, Eldar YC, Fishman EK, Fleshman J, Go VLW, Holt JM, Field B, Goldberg A, Hoos W, Iacobuzio-Donahue C, Li D, Lidgard G, Maitra A, Matrisian LM, Poblete S, Rothschild L, Sander C, Schwartz LH, Shalit U, Srivastava S, Wolpin B. Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review. Pancreas. 2021 Mar 1;50(3):251-279. doi: 10.1097/MPA.0000000000001762.
- Background: PubMed 34002083Klein AP. Pancreatic cancer epidemiology: understanding the role of lifestyle and inherited risk factors. Nat Rev Gastroenterol Hepatol. 2021 Jul;18(7):493-502. doi: 10.1038/s41575-021-00457-x. Epub 2021 May 17.
- Background: PubMed 31386141US Preventive Services Task Force; Owens DK, Davidson KW, Krist AH, Barry MJ, Cabana M, Caughey AB, Curry SJ, Doubeni CA, Epling JW Jr, Kubik M, Landefeld CS, Mangione CM, Pbert L, Silverstein M, Simon MA, Tseng CW, Wong JB. Screening for Pancreatic Cancer: US Preventive Services Task Force Reaffirmation Recommendation Statement. JAMA. 2019 Aug 6;322(5):438-444. doi: 10.1001/jama.2019.10232.
- Results publication: PubMed 37985692Cao K, Xia Y, Yao J, Han X, Lambert L, Zhang T, Tang W, Jin G, Jiang H, Fang X, Nogues I, Li X, Guo W, Wang Y, Fang W, Qiu M, Hou Y, Kovarnik T, Vocka M, Lu Y, Chen Y, Chen X, Liu Z, Zhou J, Xie C, Zhang R, Lu H, Hager GD, Yuille AL, Lu L, Shao C, Shi Y, Zhang Q, Liang T, Zhang L, Lu J. Large-scale pancreatic cancer detection via non-contrast CT and deep learning. Nat Med. 2023 Dec;29(12):3033-3043. doi: 10.1038/s41591-023-02640-w. Epub 2023 Nov 20.
- Individual participant dataSharing planned: No
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RecruitingPhase 3
Lanreotide Versus Placebo Before Surgery to Prevent a Surgical Complication Called a Pancreatic Fistula
Pancreatic Carcinoma, Pancreatic Neoplasm
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Jin Gang, M.D. study chair
Changhai Hospital
Wang Bei Lei, M.D. study director
Changhai Hospital
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pancreatic Cancer and Pancreatic Ductal Adenocarcinoma, run by Changhai Hospital at 3 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 19 Mar 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Wang Bei Lei, M.D. (86-13774238083, lilly_wang@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Detection rate of PDAC (time frame: 3 years). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06638866, which also lists 10 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Mar 2025.